The International Workshop of Machine Learning in Clinical Neuroimaging, a satellite event of MICCAI , calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions.
In the machine learning track, we seek novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroscientific data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to:
- Big data
- Spatio-temporal brain data analysis
- Structural data analysis
- Graph theory and complex network analysis
- Longitudinal data analysis
- Model stability and interpretability
- Model scalability in large neuroimaging datasets
- Multi-source data integration and multi-view learning
- Multi-site data analysis, from preprocessing to modeling
- Domain adaptation, data harmonization, and transfer learning in neuroimaging
- Unsupervised methods for stratifying brain disorders
- Deep learning in clinical neuroimaging
- Model uncertainty in clinical predictions
In the clinical neuroimaging track, the applications of existing machine learning algorithms are evaluated to move towards precision medicine for complex brain disorders. The discovery of biological markers in medicine is an important challenge across different fields and various experimental procedures and designs are used to detect biological signatures that can be utilized for improvement in diagnostic, treatment, or for other beneficial ends. However, for most complex brain disorders, we do not have reliable biomarkers today. The application of advanced machine learning methods may help reaching this goal. Therefore, we invite the community to submit conference contributions on machine learning approaches with the goal to improve our understanding of complex brain disorders, moving the field closer towards precision medicine. Topics of interest include but are not limited to:
- Biomarker discovery
- Refinement of nosology and diagnostics
- Biological validation of clinical syndromes
- Treatment outcome prediction
- Course prediction
- Analysis of wearable sensors
- Neurogenetics and brain imaging genetics
- Mechanistic modeling
- Brain aging
- The presentation of clinical neuroimaging databases to stimulate developments in machine learning
The workshop seeks high-quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style using the CMT system. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2021 conference.
MLCN Special Issue at the MELBA journal:
This year, we will invite the top (25%) accepted papers to submit an extended version of their contribution to the MLCN special issue at the Journal of Machine Learning for Biomedical Imaging (MELBA).
Best Paper Award:
All MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality, novelty, and clarity of contributions. The winner will be announced at the end of the workshop and will receive a 500 USD honorarium.
- Paper submission deadline: June 25, 2021, 11:59 PM Pacific Time
- Notification of Acceptance: July 16, 2021
- Camera-ready Submission: July 30, 2021
- Workshop Date: September 27, 2021
For more information click "LINK TO ORIGINAL" below.